Every year prospective students search institutional websites, attend college fairs, request information, visit campuses, complete inquiry forms and eventually enter a recruitment funnel. Many of these interactions were tracked and used to shape subsequent communication. Artificial intelligence is beginning to disrupt this model.
A prospective student can now ask an AI system to recommend universities using highly individualized criteria and have the system compare programs, interpret published costs and progressively narrow choices before the student visits a university website or speaks with an admissions representative. AI is becoming an intermediary between prospective students and institutions, with tremendous influence over students’ choices before universities even know that the student exists. This influence represents both a competitive risk and an important opportunity.
1. AI is Reshaping the College Decision
Previously, search engines were used by students to obtain information, but AI is beginning to participate in the very dynamics of the college decision itself, not just by down-selecting and prioritizing choices based on student-generated criteria but by asking follow-up questions, introducing additional constraints and progressively narrowing choices. Universities, in effect, will no longer be communicating only with prospective students but also with intelligent systems acting on students’ behalf. In addition, parents, spouses, employers, high school counselors and community college advisors guide students, each perhaps using AI as part of their assessment and recommendation process with different sets of criteria.
AI is entering an already complex network of influences, and universities must think beyond individual prospect communication to the larger decision ecosystem surrounding the student. The enrollment competition thus is not only for a student’s attention but also for accurate consideration and prioritization by the AI system that students—and those advising them—use.
While the traditional enrollment funnel was not as linear as diagrams often suggest, it provided institutions with a useful approximation of student movement, from awareness to inquiry, application, admissions and matriculation. AI makes that journey substantially less visible. An institution may be eliminated from consideration without ever knowing that it was being considered because an AI system without a trackable link, inquiry form or conventional referral trail may be shaping the decision. This reality negates a large portion of previous process flows that provided institutions with valuable data, necessitating new tracking and engagement models for institutions to assess how students first learned about them, which criteria they used to compare institutions and how AI tools shaped recommendations. Information that is specific to a student’s needs now takes on new meaning, as an institution that has compelling answers to generic questions will remain largely invisible if an AI platform or agent cannot readily find, understand and compare student-specified criteria information.
2. Visibility Must Begin with Institutional Truth
The emerging strategic question for enrollment managers now becomes whether an AI system can accurately understand, differentiate and recommend their institution based on information available from widely disperse sources. This question has led to growing interest in LLM visibility, which in many ways is the equivalent of search engine optimization for large language models. Traditional search optimization focused heavily on whether an institution appeared in response to selected keywords. AI-mediated discovery depends more broadly on whether information about an institution is accessible, consistent, current, credible and sufficiently distinctive to support a recommendation placing data quality and institutional coherence at the center of enrollment strategy.
Universities routinely show conflicting information about program names, admission requirements, tuition, delivery formats, transfer policies and career outcomes across multiple web pages and systems. What might earlier have frustrated a prospective student can now distort institutional representation or exclude it from consideration by an AI system, which places a premium on reliable and current information about programs, cost, financial aid, transfer, accreditation, delivery options, student support, graduation and employment and outcomes that are consistent across institutional systems. Rankings, comparisons and appearances on common lists all take on a new relevance because of how AI systems compare data across institutions.
3. The AI-Mediated Marketplace May Not be Neutral
Even when institutional information is accurate, there is no assurance that AI-mediated recommendations will be neutral, since AI systems may omit an institution, confuse similarly named programs and institutions, rely on outdated information or over emphasize measures that are easy to obtain but poorly aligned with student needs. They may give disproportionate attention to highly ranked or nationally prominent institutions because they generate more digital content and receive more third-party coverage. This attention disparity presents a form of representational iniquity. A regional university may offer a stronger academic, financial, geographic and personal fit for a particular student but could well remain less visible because the digital record contains less information.
Digital search and social media did not remain neutral marketplaces, and there is little reason to assume that AI-mediated discovery will not do the same. Universities therefore need to monitor not only whether they appear in AI-generated searches but why particular institutions are recommended and which dimensions of value are considered.
4. Competitive Advantage Requires Human and Institutional Responsiveness
AI can help institutions answer routine questions 24/7, translate information on demand, personalize communications, identify incomplete information and prompt completion, and clarify financial aid requirements and options. While useful, a more developed strategy would connect recruitment, admissions, financial aid, transfer evaluation, advising, orientation and student success into a coherent set of engagements, using AI to identify points of friction and disengagement, provide relevant information at the appropriate time and alert institutional teams when human intervention is needed.
The institutions that succeed will not necessarily be those that deploy AI to the greatest extent but those that combine trustworthy information, a clear value proposition, responsive processes and purposeful human engagement. The distinction matters because technology can accelerate a weak process as readily as it can improve a strong one. The greatest competitive threat is not that another university acquires a better tool but that its AI-driven strategy makes it easier to understand and navigate, faster to respond and more persuasive about its value. AI should make institutional advantages visible and memorable by highlighting unique attributes rather than reducing the institution to generic claims.
AI integration makes appropriate use of human interaction even more consequential. AI is well suited to provide routine information, reminders, translation, preliminary comparisons and administrative navigation, but people remain essential when students face ambiguity, consequential choices, unusual circumstances or uncertainty about where they belong. The appropriate test is not whether AI can complete an interaction but whether it increases the student’s ability to make an informed decision and reach the right person when they need to.
Students should know when they are interacting with AI and should have an uncomplicated path to a person. Used well, AI should free enrollment staff from repetitive transactions, so they can devote more time to advising students, resolving barriers and building relationships. As automated communication becomes more common and students encounter increasingly polished messages from all universities, what they may remember is which institution listened, understood their circumstances and helped them solve an actual problem, thus placing a premium on authentic human responsiveness.
5. AI Creates Responsibilities on Both Sides of the Decision
While much has been discussed about how students may use AI in their selection process, far less attention has been paid to how institutions may use AI to identify, categorize, predict response and influence students through personalized communication, intervention strategies and resource allocation. These uses may improve responsiveness, but they also raise questions related to privacy, transparency, bias and institutional judgment. Personalization can help students navigate their choices, but the issue of surveillance, perceived or real, requires consideration, as does the possibility that a model could conclude that a student is unlikely to succeed and therefore restrict the opportunities it presents them as a means of enrollment optimization, emphasizing issues of traceability and accountability in decision making.
It is equally important to consider asymmetry among prospective students based on access to AItools, much as access to paid consultants differs today. AI could therefore narrow information and opportunity gaps or widen them, and ultimately an institution’s reputation will depend on the trust it develops, irrespective of the technology used.
6. Enrollment Success Must Expand Beyond Recruitment
The introduction of AI into enrollment strategy comes at a time when universities already face substantial pressures, ranging from demographic contraction in traditional college-going populations, greater price sensitivity, declining public confidence, competition from national universities and online providers, and increasing numbers of students balancing education with work and family responsibilities. Universities need more than a new recruiting process or addition of technology. They need to focus on whom they were designed to serve and how they may best support the region, rather than pursuing their own ambitions. AI can help institutions identify and appropriately communicate with community college transfers, adults with some college but no credentials, working professionals seeking advancement, students pursuing shorter credentials that can accumulate toward degrees and place-bound learners who need evening, weekend, hybrid or accelerated options. However, segmentation must not become stereotyping, and data must be used to expand student choice rather than to determine prematurely what they can pursue.
Enrollment strategy must connect institutional offerings to the realities of students’ lives and become more fully integrated with academic planning and student success. It is of little institutional or public value to become more effective at attracting students while leaving intact the barriers that prevent them from registering, progressing and completing a credential that ensures workplace relevance and socioeconomic mobility. The larger opportunity is not just in creating a more efficient admissions office but in establishing an integrated enrollment and success infrastructure organized around the student.
An AI-enabled enrollment strategy cannot be evaluated only through the number of inquiries, applications, deposits, yield and cost per enrollment. While those measures indicate whether an institution enrolled a student, they do not establish whether it helped the student make a sound educational decision. A more meaningful assessment would examine whether students understood actual cost and financial aid, received timely and accurate transfer credit evaluations, selected programs aligned with their goals, encountered fewer administrative barriers, persisted and completed credentials, and attained gainful employment or further education.
The New Leadership Responsibility
Institutions need to understand the implications of the deeper change occurring around them with AI-mediated recommendations upending the traditional enrollment funnel. These implications will influence which institutions become visible, what students understand as value and which educational choices appear attainable. This issue is no longer one of enrollment management or marketing; it is a matter of institutional leadership and public purpose.
Universities cannot control every answer an AI system produces, but they can ensure that published information is accurate, academic offerings are responsive to workplace and regional need, administrative processes are coherent, students can reach people when judgment matters, and AI use remains consistent with institutional values and public responsibilities. For regional public universities, the stakes are especially high. If AI favors institutions that are easiest to describe, most frequently mentioned or best able to purchase visibility rather than those in the best position to serve a particular student, it may reinforce existing hierarchies under the appearance of personalization.
Leadership must therefore extend beyond adopting AI or becoming visible within it. Institutions must help establish the conditions under which AI-mediated choice can be trusted with accurate representation, transparent mediation, meaningful human judgment and accountability for student outcomes.
AI may alter how students find the university, but it does not alter the university’s obligation to be worthy of being found.